Overview

Dataset statistics

Number of variables10
Number of observations204
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 KiB
Average record size in memory80.6 B

Variable types

NUM9
CAT1

Reproduction

Analysis started2020-11-13 02:26:07.667813
Analysis finished2020-11-13 02:26:28.881641
Duration21.21 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

DATE has unique values Unique
resConstruct_spending has unique values Unique

Variables

DATE
Categorical

UNIQUE

Distinct count204
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2012-01-01
 
1
2015-02-01
 
1
2015-01-01
 
1
2014-09-01
 
1
2004-06-01
 
1
Other values (199)
199
ValueCountFrequency (%) 
2012-01-0110.5%
 
2015-02-0110.5%
 
2015-01-0110.5%
 
2014-09-0110.5%
 
2004-06-0110.5%
 
2005-01-0110.5%
 
2016-08-0110.5%
 
2016-11-0110.5%
 
2003-12-0110.5%
 
2017-06-0110.5%
 
Other values (194)19495.1%
 
2020-11-12T21:26:29.056686image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

uspop_growth
Real number (ℝ≥0)

Distinct count17
Unique (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8036695001374382
Minimum0.5223373578996761
Maximum0.9642539171360748
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:29.324796image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.5223373579
5-th percentile0.5223373579
Q10.7200176887
median0.8278460417
Q30.9254839689
95-th percentile0.9642539171
Maximum0.9642539171
Range0.4419165592
Interquartile range (IQR)0.2054662803

Descriptive statistics

Standard deviation0.1275220537
Coefficient of variation (CV)0.1586747459
Kurtosis-0.7935385078
Mean0.8036695001
Median Absolute Deviation (MAD)0.1005770445
Skewness-0.4425544566
Sum163.948578
Variance0.01626187418
2020-11-12T21:26:29.610482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.9642539171125.9%
 
0.7275176958125.9%
 
0.8594817128125.9%
 
0.8278460417125.9%
 
0.9277974857125.9%
 
0.7200176887125.9%
 
0.9254839689125.9%
 
0.6310078932125.9%
 
0.7306411782125.9%
 
0.6867731556125.9%
 
Other values (7)8441.2%
 
ValueCountFrequency (%) 
0.5223373579125.9%
 
0.6310078932125.9%
 
0.6867731556125.9%
 
0.7166694134125.9%
 
0.7200176887125.9%
 
ValueCountFrequency (%) 
0.9642539171125.9%
 
0.9510552428125.9%
 
0.9458652873125.9%
 
0.9277974857125.9%
 
0.9254839689125.9%
 

med_hIncome
Real number (ℝ≥0)

Distinct count17
Unique (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59229.470588235294
Minimum55900.0
Maximum63179.0
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:29.799581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum55900
5-th percentile55900
Q157856
median59286
Q360178
95-th percentile63179
Maximum63179
Range7279
Interquartile range (IQR)2322

Descriptive statistics

Standard deviation2080.616045
Coefficient of variation (CV)0.03512805407
Kurtosis-0.7020095558
Mean59229.47059
Median Absolute Deviation (MAD)1430
Skewness0.1707949454
Sum12082812
Variance4328963.127
2020-11-12T21:26:29.953257image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
56006125.9%
 
56873125.9%
 
57856125.9%
 
60178125.9%
 
56969125.9%
 
61779125.9%
 
58400125.9%
 
59286125.9%
 
62626125.9%
 
59901125.9%
 
Other values (7)8441.2%
 
ValueCountFrequency (%) 
55900125.9%
 
56006125.9%
 
56873125.9%
 
56969125.9%
 
57856125.9%
 
ValueCountFrequency (%) 
63179125.9%
 
62626125.9%
 
61779125.9%
 
60985125.9%
 
60178125.9%
 

rentl_vacnyRate
Real number (ℝ≥0)

Distinct count33
Unique (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.888235294117646
Minimum6.6
Maximum11.1
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:30.131179image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum6.6
5-th percentile6.8
Q17.475
median9.4
Q39.925
95-th percentile10.6
Maximum11.1
Range4.5
Interquartile range (IQR)2.45

Descriptive statistics

Standard deviation1.311847801
Coefficient of variation (CV)0.1475937301
Kurtosis-1.251129731
Mean8.888235294
Median Absolute Deviation (MAD)0.8
Skewness-0.4122879429
Sum1813.2
Variance1.720944654
2020-11-12T21:26:30.285747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10.1188.8%
 
7157.4%
 
9.8125.9%
 
9.9125.9%
 
9.6125.9%
 
9.494.4%
 
6.894.4%
 
8.694.4%
 
10.694.4%
 
7.162.9%
 
Other values (23)9345.6%
 
ValueCountFrequency (%) 
6.631.5%
 
6.731.5%
 
6.894.4%
 
6.962.9%
 
7157.4%
 
ValueCountFrequency (%) 
11.131.5%
 
10.731.5%
 
10.694.4%
 
10.431.5%
 
10.331.5%
 

unemplt_rate
Real number (ℝ≥0)

Distinct count56
Unique (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.189705882352942
Minimum3.7
Maximum10.0
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:30.460621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile4.1
Q14.875
median5.7
Q37.525
95-th percentile9.585
Maximum10
Range6.3
Interquartile range (IQR)2.65

Descriptive statistics

Standard deviation1.779063215
Coefficient of variation (CV)0.2874229
Kurtosis-0.6808490223
Mean6.189705882
Median Absolute Deviation (MAD)1
Skewness0.7567137459
Sum1262.7
Variance3.165065923
2020-11-12T21:26:30.625305image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5146.9%
 
4.7125.9%
 
5.7104.9%
 
4.983.9%
 
4.483.9%
 
5.883.9%
 
5.483.9%
 
6.173.4%
 
973.4%
 
5.673.4%
 
Other values (46)11556.4%
 
ValueCountFrequency (%) 
3.721.0%
 
3.842.0%
 
3.910.5%
 
431.5%
 
4.142.0%
 
ValueCountFrequency (%) 
1010.5%
 
9.942.0%
 
9.842.0%
 
9.621.0%
 
9.542.0%
 

int_rate
Real number (ℝ≥0)

Distinct count25
Unique (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.000392156862745
Minimum0.5
Maximum6.25
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:30.784813image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.75
median1.25
Q32.3125
95-th percentile6.25
Maximum6.25
Range5.75
Interquartile range (IQR)1.5625

Descriptive statistics

Standard deviation1.740818799
Coefficient of variation (CV)0.8702387646
Kurtosis0.692486302
Mean2.000392157
Median Absolute Deviation (MAD)0.5
Skewness1.380410738
Sum408.08
Variance3.030450092
2020-11-12T21:26:30.969898image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.757134.8%
 
2.25167.8%
 
1.25157.4%
 
2157.4%
 
0.5146.9%
 
6.25146.9%
 
1125.9%
 
1.7562.9%
 
2.7552.5%
 
2.552.5%
 
Other values (15)3115.2%
 
ValueCountFrequency (%) 
0.5146.9%
 
0.757134.8%
 
0.8310.5%
 
1125.9%
 
1.25157.4%
 
ValueCountFrequency (%) 
6.25146.9%
 
610.5%
 
5.7531.5%
 
5.521.0%
 
5.2521.0%
 

cpi_rent
Real number (ℝ≥0)

Distinct count203
Unique (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.11552450980395
Minimum197.0
Maximum324.815
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:31.127156image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum197
5-th percentile202.075
Q1222.75
median249.109
Q3278.24475
95-th percentile315.20365
Maximum324.815
Range127.815
Interquartile range (IQR)55.49475

Descriptive statistics

Standard deviation34.99803538
Coefficient of variation (CV)0.1382690194
Kurtosis-0.8853458776
Mean253.1155245
Median Absolute Deviation (MAD)27.724
Skewness0.2584560172
Sum51635.567
Variance1224.86248
2020-11-12T21:26:31.257984image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
248.99921.0%
 
262.70710.5%
 
324.81510.5%
 
239.8510.5%
 
208.310.5%
 
248.63910.5%
 
283.1310.5%
 
250.98610.5%
 
272.73310.5%
 
257.18910.5%
 
Other values (193)19394.6%
 
ValueCountFrequency (%) 
19710.5%
 
197.710.5%
 
198.210.5%
 
198.510.5%
 
198.810.5%
 
ValueCountFrequency (%) 
324.81510.5%
 
323.96810.5%
 
322.62810.5%
 
321.53310.5%
 
320.65110.5%
 

homePrice_index
Real number (ℝ≥0)

Distinct count203
Unique (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.50988725490197
Minimum116.438
Maximum205.514
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:31.426580image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum116.438
5-th percentile126.71735
Q1143.967
median160.6725
Q3180.27575
95-th percentile196.8912
Maximum205.514
Range89.076
Interquartile range (IQR)36.30875

Descriptive statistics

Standard deviation21.93681016
Coefficient of variation (CV)0.1358233266
Kurtosis-0.8679574479
Mean161.5098873
Median Absolute Deviation (MAD)17.736
Skewness0.06677222649
Sum32948.017
Variance481.2236402
2020-11-12T21:26:31.581840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
166.67521.0%
 
158.67410.5%
 
205.51410.5%
 
174.51310.5%
 
159.16810.5%
 
173.47410.5%
 
169.54710.5%
 
159.3810.5%
 
144.08210.5%
 
165.72110.5%
 
Other values (193)19394.6%
 
ValueCountFrequency (%) 
116.43810.5%
 
116.91810.5%
 
117.93110.5%
 
119.21110.5%
 
120.7910.5%
 
ValueCountFrequency (%) 
205.51410.5%
 
205.50610.5%
 
205.44810.5%
 
205.26410.5%
 
205.06610.5%
 

newHouse_starts
Real number (ℝ≥0)

Distinct count194
Unique (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1226.0441176470588
Minimum478.0
Maximum2273.0
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:31.721596image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum478
5-th percentile547.2
Q1842.75
median1156.5
Q31649.25
95-th percentile2064.4
Maximum2273
Range1795
Interquartile range (IQR)806.5

Descriptive statistics

Standard deviation492.5248842
Coefficient of variation (CV)0.4017187287
Kurtosis-1.002186976
Mean1226.044118
Median Absolute Deviation (MAD)408.5
Skewness0.3511503715
Sum250113
Variance242580.7616
2020-11-12T21:26:31.881196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
58531.5%
 
110321.0%
 
59421.0%
 
88821.0%
 
120721.0%
 
119021.0%
 
114621.0%
 
115021.0%
 
108521.0%
 
70810.5%
 
Other values (184)18490.2%
 
ValueCountFrequency (%) 
47810.5%
 
49010.5%
 
50510.5%
 
51710.5%
 
53410.5%
 
ValueCountFrequency (%) 
227310.5%
 
220710.5%
 
215110.5%
 
214710.5%
 
214410.5%
 

resConstruct_spending
Real number (ℝ≥0)

UNIQUE

Distinct count204
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429611.6666666667
Minimum244399.0
Maximum684482.0
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-11-12T21:26:32.038722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum244399
5-th percentile251446.05
Q1317689.25
median423222
Q3539943.25
95-th percentile632795.1
Maximum684482
Range440083
Interquartile range (IQR)222254

Descriptive statistics

Standard deviation124634.8868
Coefficient of variation (CV)0.290110573
Kurtosis-1.096895431
Mean429611.6667
Median Absolute Deviation (MAD)114632
Skewness0.07596560832
Sum87640780
Variance1.553385501e+10
2020-11-12T21:26:32.235660image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
58163110.5%
 
41998910.5%
 
25118010.5%
 
40405510.5%
 
56285910.5%
 
37666110.5%
 
57975310.5%
 
54903110.5%
 
46395410.5%
 
62121910.5%
 
Other values (194)19495.1%
 
ValueCountFrequency (%) 
24439910.5%
 
24522610.5%
 
24798110.5%
 
24880810.5%
 
24892910.5%
 
ValueCountFrequency (%) 
68448210.5%
 
68126310.5%
 
67613510.5%
 
67445710.5%
 
67075710.5%
 

Interactions

2020-11-12T21:26:11.588359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:11.735758image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:11.884960image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.020513image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.304632image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.435810image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.575064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.705632image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.855372image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:12.989460image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:13.139940image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:13.304177image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:13.456572image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:13.615039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:13.769030image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:13.926282image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.077138image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.244473image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.400947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.534648image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.680168image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.817459image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:14.959962image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:15.097458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:15.238741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:15.375746image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:15.950788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:16.247575image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:16.549072image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:16.855587image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:17.001747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:17.154808image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:17.300043image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:17.542468image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:17.928389image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:18.170374image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:18.328043image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:18.498949image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:18.741115image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:18.999939image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:19.245631image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:19.457970image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:19.639928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:19.777440image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:19.925798image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:20.066232image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:20.223788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:20.635459image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:21.065892image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:21.291905image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:21.476748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:21.661220image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:21.859538image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:22.030512image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:22.186096image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:22.334520image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:22.502700image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:22.681135image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:22.854109image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:23.005724image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:23.164031image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:23.300928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:23.470822image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:23.662597image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:23.887631image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:24.095018image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:24.508702image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:24.859589image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:25.310039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:25.483588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:25.654765image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:26.028359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:26.203950image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:26.376056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:26.547960image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:26.696997image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:26.877105image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:27.044327image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:27.215721image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:27.436405image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:28.018573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-11-12T21:26:32.399544image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-12T21:26:32.864479image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-12T21:26:33.354329image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-12T21:26:33.648328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-12T21:26:28.357798image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-12T21:26:28.681700image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

DATEuspop_growthmed_hIncomerentl_vacnyRateunemplt_rateint_ratecpi_renthomePrice_indexnewHouse_startsresConstruct_spending
02002-01-010.92779759360.09.15.71.25197.0116.4381698.0382979.0
12002-02-010.92779759360.09.15.71.25197.7116.9181829.0391434.0
22002-03-010.92779759360.09.15.71.25198.2117.9311642.0390942.0
32002-04-010.92779759360.08.45.91.25198.5119.2111592.0404255.0
42002-05-010.92779759360.08.45.81.25198.8120.7901764.0399164.0
52002-06-010.92779759360.08.45.81.25199.3122.3341717.0407305.0
62002-07-010.92779759360.09.05.81.25199.8123.6871655.0410171.0
72002-08-010.92779759360.09.05.71.25200.2124.7291633.0404055.0
82002-09-010.92779759360.09.05.71.25200.7125.4931804.0400059.0
92002-10-010.92779759360.09.35.71.25201.3126.1351648.0402091.0

Last rows

DATEuspop_growthmed_hIncomerentl_vacnyRateunemplt_rateint_ratecpi_renthomePrice_indexnewHouse_startsresConstruct_spending
1942018-03-010.52233763179.07.04.02.25315.883198.6791335.0583579.0
1952018-04-010.52233763179.06.84.02.25316.763200.7231269.0582427.0
1962018-05-010.52233763179.06.83.82.25317.490202.5621334.0581631.0
1972018-06-010.52233763179.06.84.02.50318.318204.1541190.0570891.0
1982018-07-010.52233763179.07.13.82.50319.351205.0661195.0562859.0
1992018-08-010.52233763179.07.13.82.50320.651205.4481280.0553691.0
2002018-09-010.52233763179.07.13.72.75321.533205.5061246.0553579.0
2012018-10-010.52233763179.06.63.82.75322.628205.5141207.0542175.0
2022018-11-010.52233763179.06.63.72.75323.968205.2641204.0539913.0
2032018-12-010.52233763179.06.63.93.00324.815204.8641117.0528124.0